Clustering algorithms

Dataset structure

Description

DBSCAN [19]

Ÿ Grouped Hard Clustering

Ÿ No overlapping data attributes

Ÿ Micro-cluster is considered as a different group is minimum density value reached

Ÿ A data that not reached the minimum density, will treat as noise, a triangle also a shape, but it considered as noise

Expectation Maximization, EM [20]

Ÿ Tree Structured Soft Clustering

Ÿ A data could be a member of many clusters

Ÿ Micro-cluster is considered as a different group or to be placed in a greater group

Fuzzy C Means, FCM [23]

Ÿ Grouped Soft Clustering

Ÿ A data could be member of many clusters based on degree of membership, normally the border point data

HDBSCAN [21]

Ÿ Tree Structured Hard Clustering

Ÿ No overlapping data attributes

Ÿ Subclustered is not an overlapping cluster, it just placed under a greater cluster

K-MEANS [22]

Ÿ Grouped Hard Clustering

Ÿ No overlapping data attributes

Ÿ No data will be treated as outliers

Ÿ Must be have an exact number of clusters need to be form, otherwise the triangle will be the member of rectangle or round